Advertisement

Bayesian Reflectance Component Separation

  • Ramón Moreno
  • Manuel Graña
  • Alicia d’Anjou
  • Carmen Hernandez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5712)

Abstract

We work on a Bayesian approach to the estimation of the specular component of a color image, based on the Dichromatic Reflection Model (DRM). The separation of diffuse and specular components is important for color image segmentation, to allow the segmentation algorithms to work on the best estimation of the reflectance of the scene. In this work we postulate a prior and likelihood energies that model the reflectance estimation process. Minimization of the posterior energy gives the desired reflectance estimation. The approach includes the illumination color normalization and the computation of a specular free image to test the pure diffuse reflection hypothesis.

Keywords

Random Markov Field Component Separation Color Segmentation Color Image Segmentation Specular Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ma, W.-C., Hawkins, T., Peers, P., Chabert, C.-F., Weiss, M., Debevec, P.: Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination. In: Eurographics Symposium on Rendering 2007 (2007)Google Scholar
  2. 2.
    Fu, Z., Tan, R.T., Caelli, T.: Specular free spectral imaging using orthogonal subspace projection. In: 18th International Conference on Pattern Recognition, 2006. ICPR 2006, vol. 1, pp. 812–815 (2006)Google Scholar
  3. 3.
    Winkler, G.: Image analysis, random fields and dynamic Monte Carlo methods. Springer, Heidelberg (1995)CrossRefzbMATHGoogle Scholar
  4. 4.
    Hara, K., Nishino, K., Ikeuchi, K.: Light source position and reflectance estimation from a single view without the distant illumination assumption. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 493–505 (2005)CrossRefGoogle Scholar
  5. 5.
    Jensen, H.W., Marschner, S.R., Levoy, M., Hanrahan, P.: A practical model for subsurface light transport. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp. 511–518. ACM Press, New York (2001)Google Scholar
  6. 6.
    Choi, Y.-J., Yoon, K.-J., Kweon, I.S.: Illuminant chromaticity estimation using dichromatic slope and dichromatic line space. In: Korea-Japan Joint Workshop on Frontiers of Computer Vision, pp. 219–224. FCV (2005)Google Scholar
  7. 7.
    Phong, B.T.: Illumination for computer-generated images. PhD thesis, The University of Utah (1973)Google Scholar
  8. 8.
    Shafer, S.A.: Using color to separate reflection components. Color Research and Aplications 10, 43–51 (1984)Google Scholar
  9. 9.
    Tan, R.T., Nishino, K., Ikeuchi, K.: Color constancy through inverse-intensity chromaticity space. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 21(3), 321–334 (2004)CrossRefGoogle Scholar
  10. 10.
    Tan, R.T., Nishino, K., Ikeuchi, K.: Separating reflection components based on chromaticity and noise analysis. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1373–1379 (2004)CrossRefGoogle Scholar
  11. 11.
    Tan, R.T., Ikeuchi, K.: Separating reflection components of textured surfaces using a single image. In: Proceedings of Ninth IEEE International Conference on Computer Vision, 2003, October 13-16, 2003, vol. 2, pp. 870–877 (2003)Google Scholar
  12. 12.
    Tan, R.T., Ikeuchi, K.: Reflection components decomposition of textured surfaces using linear basis functions. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, June 20-25, 2005, vol. 1, pp. 125–131 (2005)Google Scholar
  13. 13.
    Tan, R.T., Ikeuchi, K.: Separating reflection components of textured surfaces using a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(2), 178–193 (2005)CrossRefGoogle Scholar
  14. 14.
    Toro, J.: Dichromatic illumination estimation without pre-segmentation. Pattern Recogn. Lett. 29, 871–877 (2008)CrossRefGoogle Scholar
  15. 15.
    Toro, J., Funt, B.: A multilinear constraint on dichromatic planes for illumination estimation. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society 16, 92–97 (2007) PMID: 17283768MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ward, G.J.: Measuring and modeling anisotropic reflection. SIGGRAPH Comput. Graph. 26, 265–272 (1992)CrossRefGoogle Scholar
  17. 17.
    Yoon, K.-J., Choi, Y., Kweon, I.S.: Fast separation of reflection components using a specularity-invariant image representation. In: 2006 IEEE International Conference on Image Processing, October 8-11, 2006, pp. 973–976 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ramón Moreno
    • 1
  • Manuel Graña
    • 1
  • Alicia d’Anjou
    • 1
  • Carmen Hernandez
    • 1
  1. 1.Grupo de Inteligencia computacionalUniversity of the Basque CountrySpain

Personalised recommendations